2017

@mastersthesis{Tani2017,
title = {Unveiling innovation dynamics in a web experiment on human creativity},
author = {Giulio Tani},
year = {2017},
date = {2017-10-31},
school = {Università La Sapienza di Roma},
abstract = {The emergence of novelties and of new trends in many different contexts ranging from arts to technology is currently a matter of interest for many studies. The availability of large crowd-sourced datasets from the web has allowed to study these dynamics in ways not possible with usual volunteer based experiments. On the other hand this kind of datasets offer little control to the researcher on which and how data are gathered. To get control over the data while keeping the advantages offered by the web, we designed and realised an experiment in the form of a web game hosted on our platform specifically built for these purposes. The game was designed to involve the players via a system of levels and rewards and allow us to collect data on how they innovate, imitate and create 2-D drawings with LEGO bricks. To gain levels players had to represent selected concepts with LEGO bricks with the possibility of taking other players? composi- tions as starting point. This experiment allowed us to gather data keeping all (or at least most part) of the influence within the experiment itself and keeping trace of most part of the interaction between players: who inspired who and, through likes and shares, how they valued compositions by other authors. We registered every action performed by the players with high temporal resolution together with every mistake or change of mind: this is a kind of information usually not available on large web datasets and yet useful to determine the dynamics. Analysing words occurrences over the 4k+ compositions and 30k+ guesses we get results in line with those kno- wn from the literature that suggests us that the dataset is self consistent. We then introduce a distance between compositions in a consistent way analysing not only shapes and colours of the compositions but also number and kind of the bricks used and proportions. This distance function was at the base of our analysis: we show the relation between creativity and influence over subsequent compositions (how they have been valued by other players), the dynamics of the emergence of novelties and how they clusterise in time. Thanks to the same distance we built an influence net- work from which we were able to derive the network characteristics such as degree distributions and clustering coefficients. The results reached so far give us the hope that even more detail of what makes a trend-setter may be unveiled with further investigation. },
keywords = {kreyon},
pubstate = {published},
tppubtype = {mastersthesis}
}

The emergence of novelties and of new trends in many different contexts ranging from arts to technology is currently a matter of interest for many studies. The availability of large crowd-sourced datasets from the web has allowed to study these dynamics in ways not possible with usual volunteer based experiments. On the other hand this kind of datasets offer little control to the researcher on which and how data are gathered. To get control over the data while keeping the advantages offered by the web, we designed and realised an experiment in the form of a web game hosted on our platform specifically built for these purposes. The game was designed to involve the players via a system of levels and rewards and allow us to collect data on how they innovate, imitate and create 2-D drawings with LEGO bricks. To gain levels players had to represent selected concepts with LEGO bricks with the possibility of taking other players? composi- tions as starting point. This experiment allowed us to gather data keeping all (or at least most part) of the influence within the experiment itself and keeping trace of most part of the interaction between players: who inspired who and, through likes and shares, how they valued compositions by other authors. We registered every action performed by the players with high temporal resolution together with every mistake or change of mind: this is a kind of information usually not available on large web datasets and yet useful to determine the dynamics. Analysing words occurrences over the 4k+ compositions and 30k+ guesses we get results in line with those kno- wn from the literature that suggests us that the dataset is self consistent. We then introduce a distance between compositions in a consistent way analysing not only shapes and colours of the compositions but also number and kind of the bricks used and proportions. This distance function was at the base of our analysis: we show the relation between creativity and influence over subsequent compositions (how they have been valued by other players), the dynamics of the emergence of novelties and how they clusterise in time. Thanks to the same distance we built an influence net- work from which we were able to derive the network characteristics such as degree distributions and clustering coefficients. The results reached so far give us the hope that even more detail of what makes a trend-setter may be unveiled with further investigation.

@mastersthesis{Pullano2017,
title = {The dynamics of social interactions in a collective creativity experiment},
author = {Giulia Pullano},
year = {2017},
date = {2017-10-31},
school = {Torino University },
abstract = {The study of the dynamics behind the emergence of novelties and inno- vation is a relatively recent field of study in complex systems, fostered by the abundance of data about the creations and sharing of artworks and about on-line activity in general. Despite this recentness, many works have been able to discover and characterise several interesting statistical patterns related to the emergence of new creative elements and a very general mathematical framework describing the collective process of di- scovering and sharing novelties come out. However, still a lot has to be discovered concerning the conditions, either historical and social, fostering the emergence of creative elements from a group of interacting individuals. From a social perspective, many hypotheses have been developed and te- sted concerning the relations between individual like the presence of ?weak ties? in social networks or the ?folding? of different social groups into a larger one sharing a common goal. Complex Systems Science has given lit- tle contributions to the understanding of how the dynamics behind social interactions contributes to foster the emergence of creativity. This work of thesis is devoted to the analysis of data collected during a collective social experiment in which individuals were asked to collaborate in the realisation of a set of LEGO bricks sculptures. The participants to the experiments were provided with particular RFID tags, developed in the framework of the SOCIOPATTERNS project, that enabled a quite precise mapping of the social interactions occurring during their activity within the experiment. The interaction with the LEGO Sculptures were similarly mapped by means of other RFID tags placed around the sculptures, and their growth in volume has been recorded with the aid of infra-red depth sensors. The RFID sensors allowed for a reconstruction of the dynamical network of social interactions between the participants in the experiment. We looked for correlations between the evolving structure of this social net- work and the growing patterns of the sculptures, spotting the local social structures more prone for a rapid growth of the volume in small amounts of times and in long term periods. In this way, we were able to identify the social patterns more fruitful in terms of ?local consensus? around the development of the collective artwork, indicating a shared vision around the actions to be performed on it. Moreover, we were able to identify how the presence of ?influential individuals? characterised by means of information spreading models favoured the growth of the sculptures in the long-term. The novelty behind the proposed approach could contribute to shed light on the phenomena related to creativity and could be useful in conceiving and designing new collecting creativity experiments. },
keywords = {kreyon},
pubstate = {published},
tppubtype = {mastersthesis}
}

The study of the dynamics behind the emergence of novelties and inno- vation is a relatively recent field of study in complex systems, fostered by the abundance of data about the creations and sharing of artworks and about on-line activity in general. Despite this recentness, many works have been able to discover and characterise several interesting statistical patterns related to the emergence of new creative elements and a very general mathematical framework describing the collective process of di- scovering and sharing novelties come out. However, still a lot has to be discovered concerning the conditions, either historical and social, fostering the emergence of creative elements from a group of interacting individuals. From a social perspective, many hypotheses have been developed and te- sted concerning the relations between individual like the presence of ?weak ties? in social networks or the ?folding? of different social groups into a larger one sharing a common goal. Complex Systems Science has given lit- tle contributions to the understanding of how the dynamics behind social interactions contributes to foster the emergence of creativity. This work of thesis is devoted to the analysis of data collected during a collective social experiment in which individuals were asked to collaborate in the realisation of a set of LEGO bricks sculptures. The participants to the experiments were provided with particular RFID tags, developed in the framework of the SOCIOPATTERNS project, that enabled a quite precise mapping of the social interactions occurring during their activity within the experiment. The interaction with the LEGO Sculptures were similarly mapped by means of other RFID tags placed around the sculptures, and their growth in volume has been recorded with the aid of infra-red depth sensors. The RFID sensors allowed for a reconstruction of the dynamical network of social interactions between the participants in the experiment. We looked for correlations between the evolving structure of this social net- work and the growing patterns of the sculptures, spotting the local social structures more prone for a rapid growth of the volume in small amounts of times and in long term periods. In this way, we were able to identify the social patterns more fruitful in terms of ?local consensus? around the development of the collective artwork, indicating a shared vision around the actions to be performed on it. Moreover, we were able to identify how the presence of ?influential individuals? characterised by means of information spreading models favoured the growth of the sculptures in the long-term. The novelty behind the proposed approach could contribute to shed light on the phenomena related to creativity and could be useful in conceiving and designing new collecting creativity experiments.

@article{sakellariou2016maximum,
title = {Maximum entropy models capture melodic styles},
author = { Jason Sakellariou and Francesca Tria and Vittorio Loreto and François Pachet},
editor = {Nature Publishing Group},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569059/},
year = {2017},
date = {2017-01-01},
journal = {Scientific Reports},
volume = {7},
abstract = {We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity.},
keywords = {kreyon, loreto, tria},
pubstate = {published},
tppubtype = {article}
}

We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity.

@article{Monechi2017,
title = {Trainstopping: modelling delays dynamics on railways networks},
author = {Bernardo Monechi and Pietro Gravino and Riccardo Di Clemente},
url = {https://arxiv.org/abs/1707.08632},
year = {2017},
date = {2017-01-01},
journal = {submitted for publication to EPJ Data Science (2017)},
abstract = {Railways are a key infrastructure for any modern country, so that their state of development has even been used as a significant indicator of a country’s economic advancement. Moreover, their importance has been growing in the last decades either because of the growing Railway Traffic and to governments investments, aiming at exploiting railways means to reduce CO2 emissions and hence global warming. To the present day, many extreme events (i.e. major disruptions and large delays compromising the correct functioning of the system) occurs on a daily basis. However these phenomena have been approached, so far, from a transportation engineering point of view while a general theoretical understanding is still lacking. A better comprehension of these critical situation from a theoretical point of view could be undoubtedly useful in order to improve traffic handling policies. In this work we move toward this comprehension by proposing a model about train dynamics on railways network aiming to unveil how delays spawn and spread among the network. Inspired by models for epidemic spreading, we model the diffusion of delays among train as the diffusion of a contagion among a population of moving individuals. We built and tested our model using two large dataset about Italian and German railway traffic, collected using APIs intended to give passengers information about the trains, the state of the service and train delays. The model reproduces adequately delays dynamics in both systems, meaning that it captures the underlying key factors. In particular, our model predicts that the insurgence of clusters of stations with large delays is not due to external factors, but mainly to the interaction between different trains. Also, through our model is capable to give a quantitative account of the difference between the two considered railway systems in terms of probability of contagion and delays dynamics.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {article}
}

Railways are a key infrastructure for any modern country, so that their state of development has even been used as a significant indicator of a country’s economic advancement. Moreover, their importance has been growing in the last decades either because of the growing Railway Traffic and to governments investments, aiming at exploiting railways means to reduce CO2 emissions and hence global warming. To the present day, many extreme events (i.e. major disruptions and large delays compromising the correct functioning of the system) occurs on a daily basis. However these phenomena have been approached, so far, from a transportation engineering point of view while a general theoretical understanding is still lacking. A better comprehension of these critical situation from a theoretical point of view could be undoubtedly useful in order to improve traffic handling policies. In this work we move toward this comprehension by proposing a model about train dynamics on railways network aiming to unveil how delays spawn and spread among the network. Inspired by models for epidemic spreading, we model the diffusion of delays among train as the diffusion of a contagion among a population of moving individuals. We built and tested our model using two large dataset about Italian and German railway traffic, collected using APIs intended to give passengers information about the trains, the state of the service and train delays. The model reproduces adequately delays dynamics in both systems, meaning that it captures the underlying key factors. In particular, our model predicts that the insurgence of clusters of stations with large delays is not due to external factors, but mainly to the interaction between different trains. Also, through our model is capable to give a quantitative account of the difference between the two considered railway systems in terms of probability of contagion and delays dynamics.

@article{cuskley2017regularity,
title = {The regularity game: Investigating linguistic rule dynamics in a population of interacting agents},
author = { Christine Cuskley and Claudio Castellano and Francesca Colaiori and Vittorio Loreto and Martina Pugliese and Francesca Tria},
url = {http://www.sciencedirect.com/science/article/pii/S0010027716302670},
year = {2017},
date = {2017-01-01},
journal = {Cognition},
volume = {159},
pages = {25--32},
publisher = {Elsevier},
abstract = {Rules are an efficient feature of natural languages which allow speakers to use a finite set of instructions to generate a virtually infinite set of utterances. Yet, for many regular rules, there are irregular exceptions. There has been lively debate in cognitive science about how individual learners acquire rules and exceptions; for example, how they learn the past tense of preach is preached, but for teach it is taught. However, for most population or language-level models of language structure, particularly from the perspective of language evolution, the goal has generally been to examine how languages evolve stable structure, and neglects the fact that in many cases, languages exhibit exceptions to structural rules. We examine the dynamics of regularity and irregularity across a population of interacting agents to investigate how, for example, the irregular teach coexists beside the regular preach in a dynamic language system. Models show that in the absence of individual biases towards either regularity or irregularity, the outcome of a system is determined entirely by the initial condition. On the other hand, in the presence of individual biases, rule systems exhibit frequency dependent patterns in regularity reminiscent of patterns found in natural language. We implement individual biases towards regularity in two ways: through ?child? agents who have a preference to generalise using the regular form, and through a memory constraint wherein an agent can only remember an irregular form for a finite time period. We provide theoretical arguments for the prediction of a critical frequency below which irregularity cannot persist in terms of the duration of the finite time period which constrains agent memory. Further, within our framework we also find stable irregularity, arguably a feature of most natural languages not accounted for in many other cultural models of language structure.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {article}
}

Rules are an efficient feature of natural languages which allow speakers to use a finite set of instructions to generate a virtually infinite set of utterances. Yet, for many regular rules, there are irregular exceptions. There has been lively debate in cognitive science about how individual learners acquire rules and exceptions; for example, how they learn the past tense of preach is preached, but for teach it is taught. However, for most population or language-level models of language structure, particularly from the perspective of language evolution, the goal has generally been to examine how languages evolve stable structure, and neglects the fact that in many cases, languages exhibit exceptions to structural rules. We examine the dynamics of regularity and irregularity across a population of interacting agents to investigate how, for example, the irregular teach coexists beside the regular preach in a dynamic language system. Models show that in the absence of individual biases towards either regularity or irregularity, the outcome of a system is determined entirely by the initial condition. On the other hand, in the presence of individual biases, rule systems exhibit frequency dependent patterns in regularity reminiscent of patterns found in natural language. We implement individual biases towards regularity in two ways: through ?child? agents who have a preference to generalise using the regular form, and through a memory constraint wherein an agent can only remember an irregular form for a finite time period. We provide theoretical arguments for the prediction of a critical frequency below which irregularity cannot persist in terms of the duration of the finite time period which constrains agent memory. Further, within our framework we also find stable irregularity, arguably a feature of most natural languages not accounted for in many other cultural models of language structure.

@periodical{Pompei2017,
title = {Copystree: gaming artificial phylogenies},
author = {Simone Pompei and Vittorio Loreto and Francesca Tria},
url = {http://www.socialdynamics.it/pubs/},
year = {2017},
date = {2017-01-01},
issuetitle = {Capturing Phylogenetic Algorithms for Linguistics},
journal = {Language Dynamics and Change},
abstract = {The reconstruction of phylogenies of cultural artefacts represents an open problem that mixes theoretical and computational challenges. Existing bench- marks rely on simulated phylogenies, where hypotheses on the underlying evolutionary mechanisms are unavoidable, or in real data phylogenies, for which no true evolutionary history is known. Here we introduce a web-based game, Copystree, where users create phylogenies of manuscripts, through successive copying actions, in a fully monitored setup. While players enjoy the experience, Copystree allows to build artificial phylogenies whose evolutionary processes do not obey to any pre-defined theoretical mechanisms, being generated instead with the unpredictability of human creativity. We present the analysis of the data gathered during the first set of experiments and use the artificial phylogenies gathered for a first test of existing phylogenetic algorithms.},
keywords = {kreyon, loreto},
pubstate = {published},
tppubtype = {periodical}
}

The reconstruction of phylogenies of cultural artefacts represents an open problem that mixes theoretical and computational challenges. Existing bench- marks rely on simulated phylogenies, where hypotheses on the underlying evolutionary mechanisms are unavoidable, or in real data phylogenies, for which no true evolutionary history is known. Here we introduce a web-based game, Copystree, where users create phylogenies of manuscripts, through successive copying actions, in a fully monitored setup. While players enjoy the experience, Copystree allows to build artificial phylogenies whose evolutionary processes do not obey to any pre-defined theoretical mechanisms, being generated instead with the unpredictability of human creativity. We present the analysis of the data gathered during the first set of experiments and use the artificial phylogenies gathered for a first test of existing phylogenetic algorithms.

Coping with the complexities of the social world in the 21st century requires deeper quantitative and predictive understanding. Forty-three internationally acclaimed scientists and thinkers share their vision for complexity science in the next decade in this invaluable book. Topics cover how complexity and big data science could help society to tackle the great challenges ahead, and how the newly established Complexity Science Hub Vienna might be a facilitator on this path.

@inbook{Loreto2016,
title = {Dynamics on Expanding Spaces: Modeling the Emergence of Novelties},
author = {V Loreto, VDP Servedio, SH Strogatz, F Tria},
editor = {Mirko Degli Esposti, Eduardo G. Altmann, François Pachet},
url = {http://link.springer.com/chapter/10.1007%2F978-3-319-24403-7_5},
doi = {10.1007/978-3-319-24403-7_5},
isbn = {978-3-319-24401-3},
year = {2016},
date = {2016-05-19},
booktitle = {Creativity and Universality in Language},
pages = {59-83},
publisher = {Springer International Publishing},
series = {Lecture Notes in Morphogenesis},
abstract = {Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, and experiment with new situations. Occasionally, we as individual, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological, and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon’s model tracing back to the 1950s, to the newest model of Polya’s urn with triggering of one novelty by another. What seems to be key in the successful modeling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, and technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically, it is very interesting to look at the consequences of the interplay between the “actual” and the “possible” and this is the aim of this short review.},
keywords = {adjacent possible, innovation_dynamics, kreyon, loreto, review, servedio, strogatz, tria},
pubstate = {published},
tppubtype = {inbook}
}

Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, and experiment with new situations. Occasionally, we as individual, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological, and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon’s model tracing back to the 1950s, to the newest model of Polya’s urn with triggering of one novelty by another. What seems to be key in the successful modeling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, and technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically, it is very interesting to look at the consequences of the interplay between the “actual” and the “possible” and this is the aim of this short review.

@inproceedings{Mastroianni2016,
title = {Individual Mobility Patterns in Urban Environment},
author = {Pierpaolo Mastroianni and Bernardo Monechi and Vito DP Servedio and Carlo Liberto and Gaetano Valenti and Vittorio Loreto},
url = {https://www.researchgate.net/profile/Bernardo_Monechi/publication/302973966_Individual_Mobility_Patterns_in_Urban_Environment/links/57c4454808aee50192e89a98.pdf},
year = {2016},
date = {2016-04-22},
booktitle = {e COMPLEXIS 2016, 1st
International Conference on Complex Information Systems, Rome, 22-24
April 2016},
abstract = {The understanding and the characterisation of individual mobility patterns in urban environments is important in order to improve liveability and planning of big cities. In relatively recent times, the availability of data regarding human movements have fostered the emergence of a new branch of social studies, with the aim to unveil and study those patterns thanks to data collected by means of geolocalisation technologies. In this paper we analyse a large dataset of GPS tracks of cars collected in Rome (Italy). Dividing the drivers in classes according to the number of trips they perform in a day, we show that the sequence of the travelled space connecting two consecutive stops shows a precise behaviour so that the shortest trips are performed at the middle of the sequence, when the longest occur at the beginning and at the end when drivers head back home. We show that this behaviour is consistent with the idea of an optimisation process in which the total travel time is minimised, under the effect of spatial constraints so that the starting points is on the border of the space in which the dynamics takes place.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {inproceedings}
}

The understanding and the characterisation of individual mobility patterns in urban environments is important in order to improve liveability and planning of big cities. In relatively recent times, the availability of data regarding human movements have fostered the emergence of a new branch of social studies, with the aim to unveil and study those patterns thanks to data collected by means of geolocalisation technologies. In this paper we analyse a large dataset of GPS tracks of cars collected in Rome (Italy). Dividing the drivers in classes according to the number of trips they perform in a day, we show that the sequence of the travelled space connecting two consecutive stops shows a precise behaviour so that the shortest trips are performed at the middle of the sequence, when the longest occur at the beginning and at the end when drivers head back home. We show that this behaviour is consistent with the idea of an optimisation process in which the total travel time is minimised, under the effect of spatial constraints so that the starting points is on the border of the space in which the dynamics takes place.

@inproceedings{Gravino2016b,
title = {Unveiling political opinion structures with a web-experiment},
author = {Pietro Gravino and Saverio Caminiti and Alina Sirbu and Francesca Tria and
Vito D. P. Servedio and Vittorio Loreto},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005906300390047},
year = {2016},
date = {2016-04-22},
booktitle = {COMPLEXIS 2016, 1st International Conference on Complex Information Systems, Rome, 22-24 April 2016},
abstract = {The dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos`ı) where we explicitly investigate participants’ opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {inproceedings}
}

The dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos`ı) where we explicitly investigate participants’ opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.

@article{Monechi2016,
title = {Waves of Novelties in the Expansion into the Adjacent Possible},
author = {Bernardo Monechi and Alvaro Ruiz-Serrano and Francesca Tria and Vittorio Loreto },
editor = {Public Library of Science},
url = {http://www.socialdynamics.it/pubs/},
year = {2016},
date = {2016-03-21},
journal = {PloS one},
volume = {12},
number = {6},
pages = {e0179303},
abstract = {The emergence of novelties and their rise and fall in popularity is an ubiquitous phenomenon in human activities. The coexistence of always popular milestones with novel and sometimes ephemeral trends pervades technological, scientific and artistic production. By introducing suitable statistical measures, we demonstrate that different systems of human activities, i.e. the creation of hashtags in Twitter, the interaction with online program code repositories, the creation of texts and the listening of songs on an on-line platform, exhibit surprisingly similar properties.
We then introduce a general framework to explain those regularities. We propose a simple mathematical model based on the expansion into the adjacent possible, that has been proven to be a very general and powerful mechanism able to explain many of the statistical patterns emerging in innovation dynamics, to which we add two crucial elements. On the one hand we quantify the idea that, while exploring a conceptual or physical space, inertia exists towards known already discovered elements. On the other hand, we highlight the role of the collective dynamics - where many users interact, in a direct or indirect way in the emergence and diffusion of novelties and innovations. },
keywords = {adjacent possible, complex systems, innovation, kreyon, popularity, trends},
pubstate = {published},
tppubtype = {article}
}

The emergence of novelties and their rise and fall in popularity is an ubiquitous phenomenon in human activities. The coexistence of always popular milestones with novel and sometimes ephemeral trends pervades technological, scientific and artistic production. By introducing suitable statistical measures, we demonstrate that different systems of human activities, i.e. the creation of hashtags in Twitter, the interaction with online program code repositories, the creation of texts and the listening of songs on an on-line platform, exhibit surprisingly similar properties.
We then introduce a general framework to explain those regularities. We propose a simple mathematical model based on the expansion into the adjacent possible, that has been proven to be a very general and powerful mechanism able to explain many of the statistical patterns emerging in innovation dynamics, to which we add two crucial elements. On the one hand we quantify the idea that, while exploring a conceptual or physical space, inertia exists towards known already discovered elements. On the other hand, we highlight the role of the collective dynamics - where many users interact, in a direct or indirect way in the emergence and diffusion of novelties and innovations.

@article{Monechi2016,
title = {Significance and Popularity in Music Production Paper},
author = {Bernardo Monechi and Pietro Gravino and Vito DP Servedio and Francesca Tria and Vittorio Loreto },
editor = {The Royal Society},
url = {http://xtribe.eu/sites/default/files/kreyon_files/Significance_and_Popularity_in_Music_Production.pdf},
year = {2016},
date = {2016-03-05},
journal = {Open Science},
volume = {4},
number = {7},
pages = {170433},
publisher = {submitted to PLoS ONE},
abstract = {In the world of creative productions there is a constant struggle to achieve fame and popularity. While highly popular creations are usually well remembered throughout the years, many influential works that did not achieve that status are long-forgotten. Due to their relevance for the whole artistic production, it is important to identify them and save their memory for obvious cultural reasons. In this paper we focus on the musical context and we analyze the dynamics of the tagging process on Last.fm, an on-line catalog of music albums. We define a set of general metrics aiming at characterizing the creative potential and the long-term significance of creative products and we apply them to the case of musical albums. We then adopt these metrics to implement an automated prediction method of both the commercial success of a creation and its belonging to expert validated lists of particularly creative and important works. We show that our metrics are not only useful to asses such predictions, but can also highlight important differences between culturally relevant and simply popular products of human creative production.},
keywords = {complex networks, creativity, kreyon, music, popularity},
pubstate = {published},
tppubtype = {article}
}

In the world of creative productions there is a constant struggle to achieve fame and popularity. While highly popular creations are usually well remembered throughout the years, many influential works that did not achieve that status are long-forgotten. Due to their relevance for the whole artistic production, it is important to identify them and save their memory for obvious cultural reasons. In this paper we focus on the musical context and we analyze the dynamics of the tagging process on Last.fm, an on-line catalog of music albums. We define a set of general metrics aiming at characterizing the creative potential and the long-term significance of creative products and we apply them to the case of musical albums. We then adopt these metrics to implement an automated prediction method of both the commercial success of a creation and its belonging to expert validated lists of particularly creative and important works. We show that our metrics are not only useful to asses such predictions, but can also highlight important differences between culturally relevant and simply popular products of human creative production.

@proceedings{Gravino2016,
title = {Crossing the horizon: exploring the adjacent possible in a cultural system},
author = {Pietro Gravino and Bernardo Monechi and Vito DP Servedio and Francesca Tria and Vittorio Loreto},
url = {http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Crossing-the-horizon.pdf},
year = {2016},
date = {2016-03-05},
journal = {submitted to "ICCC 2016 - The Seventh International Conference on Computational Creativity"},
publisher = {Proceedings of the Seventh International Conference on Computational Creativity, June 2016},
abstract = {It is common opinion that many innovations are triggered by serendipity whose notion is associated with fortuitous events leading to unintended consequences. One might argue that this interpretation is due to the poor understanding of the dynamics of innovations. Very little is known, in fact, about how innovations proceed and samples the space of potential novelties. This space is usually referred to as the adjacent possible, a concept originally introduced in the study of biological systems to indicate the set of possibilities that are one step away from what actually exists. In this paper we focus on the problem of defining the adjacent possible space, and analyzing its dynamics, for a particular system, namely the cultural system of the network of movies. We synthesized to this end the graph emerging from the Internet Movies Database (IMDb) and looked at the static and dynamical properties of this network. We deal, in particular, with the subtle mechanism of the adjacent possible by measuring the expansion and the coverage of this elusive space during the global evolution of the system. Finally, we introduce the concept of adjacent possibilities at the level of single node and try to elucidate its nature by looking at the correlations with topological and user annotation metrics.},
keywords = {adjacent possible, complex network, creativity, innovation_dynamics, kreyon, movies},
pubstate = {published},
tppubtype = {proceedings}
}

It is common opinion that many innovations are triggered by serendipity whose notion is associated with fortuitous events leading to unintended consequences. One might argue that this interpretation is due to the poor understanding of the dynamics of innovations. Very little is known, in fact, about how innovations proceed and samples the space of potential novelties. This space is usually referred to as the adjacent possible, a concept originally introduced in the study of biological systems to indicate the set of possibilities that are one step away from what actually exists. In this paper we focus on the problem of defining the adjacent possible space, and analyzing its dynamics, for a particular system, namely the cultural system of the network of movies. We synthesized to this end the graph emerging from the Internet Movies Database (IMDb) and looked at the static and dynamical properties of this network. We deal, in particular, with the subtle mechanism of the adjacent possible by measuring the expansion and the coverage of this elusive space during the global evolution of the system. Finally, we introduce the concept of adjacent possibilities at the level of single node and try to elucidate its nature by looking at the correlations with topological and user annotation metrics.

@inproceedings{evolang11_133,
title = {The Evolution Of Collaborative Stories},
author = { Christine Cuskley and Bernardo Monechi and Pietro Gravino and Vittorio Loreto},
editor = {S.G. Roberts and C. Cuskley and L. McCrohon and L. Barceló-Coblijn and O. Fehér and T. Verhoef},
url = {http://evolang.org/neworleans/papers/133.html},
year = {2016},
date = {2016-01-01},
booktitle = {The Evolution of Language: Proceedings of the 11th International Conference (EVOLANGX11)},
abstract = {Studies in literature and narrative have begun to argue more forcefully for considering human evolution as central to understanding stories and storytelling more generally (Sugiyama, 2001; Hernadi, 2002). However, empirical studies in language evolution have focused primarily on language structure or the language faculty, leaving the evolution of stories largely unexplored (although see Von Heiseler, 2014). Stories are unique products of human culture enabled principally by human language. Given this, the dynamics of creativity in stories, and the traits which make successful stories, are of crucial interest to understanding the evolution of language in the context of human evolution more broadly. The current work aims to illuminate how stories emerge, evolve, and change in the context of a collaborative cultural effort. We present results from a novel experimental paradigm centered around a story game where players write short continuations (between 60 and 120 characters) of existing stories. These continuations then become open to other players to continue in turn. Stories are subject to player selection, allowing for variation and speciation of the resulting narratives, and evolve as a result of collaborative effort between players. The game starts with a seed of over 60 potential stories, and players choose which stories to continue, providing a player-driven story selection mechanism. In this way, stories which are creative, intriguing, and open ended spawn more stories, and eventually lead to longer story paths as play continues. The game also introduces further limitations by constraining a players’ view of the story path: players have access only to a story and its parent, meaning knowledge of the existing narrative is limited. We present data from hundreds of players and stories, creating large story trees which explore the space of different possible narratives which grow out of a confined set of starting points. This data allows us to investigate several aspects of the growing story trees to illuminate not only what makes a story successful, but how creative stories trigger new stories, and what makes individual storytellers successful. Given the selection mechanism central to game play, we identify the most successful stories by their number of offspring. Particularly successful storytellers emerge measured both by how many children their stories have spawned, and also how long their story path extends. We also show that coherent stories often emerge, despite the fact that they are authored by several different players, and any given player only sees a limited snapshot of the story path. We contextualise the results of the game and connect it to language evolution in two ways. First, we look for detectable triggers of innovation and creativity within the story trees, and identify these as expanding the adjacent possible (e.g., new adaptations open the space of other possible adaptations in the future; Tria, Loreto, Servedio, & Strogatz, 2014). We argue that this concept can be extended to stories, using evidence from the game bolstered by evidence from more traditional literature (the Gutenberg Corpus). Second, we frame the results in terms of recurring themes found in storytelling cross-culturally (Tehrani, 2013). We suggest that the most successful triggers of innovation in stories combine original novelty and a firm grounding in existing recurring story frameworks in human culture. This indicates that much like other cultural and biological systems, stories are subject to competing pressures for stability and conservation on the one hand, and innovation and novelty on the other.},
keywords = {kreyon creastoria web-games creativity},
pubstate = {published},
tppubtype = {inproceedings}
}

Studies in literature and narrative have begun to argue more forcefully for considering human evolution as central to understanding stories and storytelling more generally (Sugiyama, 2001; Hernadi, 2002). However, empirical studies in language evolution have focused primarily on language structure or the language faculty, leaving the evolution of stories largely unexplored (although see Von Heiseler, 2014). Stories are unique products of human culture enabled principally by human language. Given this, the dynamics of creativity in stories, and the traits which make successful stories, are of crucial interest to understanding the evolution of language in the context of human evolution more broadly. The current work aims to illuminate how stories emerge, evolve, and change in the context of a collaborative cultural effort. We present results from a novel experimental paradigm centered around a story game where players write short continuations (between 60 and 120 characters) of existing stories. These continuations then become open to other players to continue in turn. Stories are subject to player selection, allowing for variation and speciation of the resulting narratives, and evolve as a result of collaborative effort between players. The game starts with a seed of over 60 potential stories, and players choose which stories to continue, providing a player-driven story selection mechanism. In this way, stories which are creative, intriguing, and open ended spawn more stories, and eventually lead to longer story paths as play continues. The game also introduces further limitations by constraining a players’ view of the story path: players have access only to a story and its parent, meaning knowledge of the existing narrative is limited. We present data from hundreds of players and stories, creating large story trees which explore the space of different possible narratives which grow out of a confined set of starting points. This data allows us to investigate several aspects of the growing story trees to illuminate not only what makes a story successful, but how creative stories trigger new stories, and what makes individual storytellers successful. Given the selection mechanism central to game play, we identify the most successful stories by their number of offspring. Particularly successful storytellers emerge measured both by how many children their stories have spawned, and also how long their story path extends. We also show that coherent stories often emerge, despite the fact that they are authored by several different players, and any given player only sees a limited snapshot of the story path. We contextualise the results of the game and connect it to language evolution in two ways. First, we look for detectable triggers of innovation and creativity within the story trees, and identify these as expanding the adjacent possible (e.g., new adaptations open the space of other possible adaptations in the future; Tria, Loreto, Servedio, & Strogatz, 2014). We argue that this concept can be extended to stories, using evidence from the game bolstered by evidence from more traditional literature (the Gutenberg Corpus). Second, we frame the results in terms of recurring themes found in storytelling cross-culturally (Tehrani, 2013). We suggest that the most successful triggers of innovation in stories combine original novelty and a firm grounding in existing recurring story frameworks in human culture. This indicates that much like other cultural and biological systems, stories are subject to competing pressures for stability and conservation on the one hand, and innovation and novelty on the other.

@inproceedings{evolang11_89,
title = {Modeling The Emergence Of Creole Languages},
author = { Francesca Tria and Vittorio Loreto and Vito Servedio and S. Mufwene Salikoko},
editor = {S.G. Roberts and C. Cuskley and L. McCrohon and L. Barceló-Coblijn and O. Fehér and T. Verhoef},
url = {http://evolang.org/neworleans/papers/89.html},
year = {2016},
date = {2016-01-01},
booktitle = {The Evolution of Language: Proceedings of the 11th International Conference (EVOLANGX11)},
abstract = {Creole languages offer an invaluable opportunity to study the processes leading to the emergence and evolution of Language, thanks to the short - typically a few generations - and reasonably well defined time-scales involved in their emergence. Another well-known case of a very fast emergence of a Language, though referring to a much smaller population size and different ecological conditions, is that of the Nicaraguan Sign Language. What these two phenomena have in common is that in both cases what is emerging is a contact language, i.e., a language born out of the non-trivial interaction of two (or more) parent languages. This is a typical case of what is known in biology as horizontal transmission. In many well-documented cases, creoles emerged in large segregated sugarcane or rice plantations on which the slave labourers were the overwhelming majority. Lacking a common substrate language, slaves were naturally brought to shift to the economically and politically dominant European language (often referred to as the lexifier) to bootstrap an effective communication system among themselves. Here, we focus on the emergence of creole languages originated in the contacts of European colonists and slaves during the 17th and 18th centuries in exogenous plantation colonies of especially the Atlantic and Indian Ocean, where detailed census data are available. Those for several States of USA can be found at http://www.census.gov/history, while for Central America and the Caribbean can be found at http://www.jamaicanfamilysearch.com/Samples/1790al11.htm. Without entering in the details of the creole formation at a fine-grained linguistic level, we aim at uncovering some of the general mechanisms that determine the emergence of contact languages, and that successfully apply to the case of creole formation.},
keywords = {kreyon, language_dynamics, language_games, loreto, servedio, tria},
pubstate = {published},
tppubtype = {inproceedings}
}

Creole languages offer an invaluable opportunity to study the processes leading to the emergence and evolution of Language, thanks to the short - typically a few generations - and reasonably well defined time-scales involved in their emergence. Another well-known case of a very fast emergence of a Language, though referring to a much smaller population size and different ecological conditions, is that of the Nicaraguan Sign Language. What these two phenomena have in common is that in both cases what is emerging is a contact language, i.e., a language born out of the non-trivial interaction of two (or more) parent languages. This is a typical case of what is known in biology as horizontal transmission. In many well-documented cases, creoles emerged in large segregated sugarcane or rice plantations on which the slave labourers were the overwhelming majority. Lacking a common substrate language, slaves were naturally brought to shift to the economically and politically dominant European language (often referred to as the lexifier) to bootstrap an effective communication system among themselves. Here, we focus on the emergence of creole languages originated in the contacts of European colonists and slaves during the 17th and 18th centuries in exogenous plantation colonies of especially the Atlantic and Indian Ocean, where detailed census data are available. Those for several States of USA can be found at http://www.census.gov/history, while for Central America and the Caribbean can be found at http://www.jamaicanfamilysearch.com/Samples/1790al11.htm. Without entering in the details of the creole formation at a fine-grained linguistic level, we aim at uncovering some of the general mechanisms that determine the emergence of contact languages, and that successfully apply to the case of creole formation.

@inproceedings{evolang11_119,
title = {The Emergence Of Rules And Exceptions In A Population Of Interacting Agents},
author = { Christine Cuskley and Vittorio Loreto},
editor = {S.G. Roberts and C. Cuskley and L. McCrohon and L. Barceló-Coblijn and O. Fehér and T. Verhoef},
url = {http://evolang.org/neworleans/papers/119.html},
year = {2016},
date = {2016-01-01},
booktitle = {The Evolution of Language: Proceedings of the 11th International Conference (EVOLANGX11)},
abstract = {Rules are an efficient feature of natural languages which allow speakers to use a finite set of instructions to generate a virtually infinite set of utterances. Yet, for many regular rules, there are irregular exceptions. There has been lively debate in cognitive science about how individual learners acquire rules and exceptions; for example, how they learn the past tense of preach is preached, but for teach it is taught. In this paper, we take a different perspective, examining the dynamics of regularity and irregularity across a population of interacting agents to investigate how inflectional rules are applied to verbs. We show that in the absence of biases towards either regularity or irregularity, the outcome is determined by the initial condition, irrespective of the frequency of usage of the given lemma. On the other hand, in presence of biases, rule systems exhibit frequency dependent patterns in regularity reminiscent of patterns in natural language corpora. We examine the case where individuals are biased towards linguistic regularity in two ways: either as child learners, or through a memory constraint wherein irregular forms can only be remembered by an individual agent for a finite time period. We provide theoretical arguments for the prediction of a critical frequency below which irregularity cannot persist in terms of the duration of the finite time period which constrains agent memory.},
keywords = {cuskley, kreyon, language_dynamics, language_games, loreto, modeling, rules},
pubstate = {published},
tppubtype = {inproceedings}
}

Rules are an efficient feature of natural languages which allow speakers to use a finite set of instructions to generate a virtually infinite set of utterances. Yet, for many regular rules, there are irregular exceptions. There has been lively debate in cognitive science about how individual learners acquire rules and exceptions; for example, how they learn the past tense of preach is preached, but for teach it is taught. In this paper, we take a different perspective, examining the dynamics of regularity and irregularity across a population of interacting agents to investigate how inflectional rules are applied to verbs. We show that in the absence of biases towards either regularity or irregularity, the outcome is determined by the initial condition, irrespective of the frequency of usage of the given lemma. On the other hand, in presence of biases, rule systems exhibit frequency dependent patterns in regularity reminiscent of patterns in natural language corpora. We examine the case where individuals are biased towards linguistic regularity in two ways: either as child learners, or through a memory constraint wherein irregular forms can only be remembered by an individual agent for a finite time period. We provide theoretical arguments for the prediction of a critical frequency below which irregularity cannot persist in terms of the duration of the finite time period which constrains agent memory.

@mastersthesis{Gelardi2016,
title = {Analysis of the Structure and the Collaborative Dynamics of GitHub Projects},
author = {Valeria Gelardi},
url = {http://www.socialdynamics.it/pubs/},
year = {2016},
date = {2016-01-01},
school = {Undergraduate thesis at Torino University},
abstract = {The recent spread of social networks and ICT systems has allowed for a huge availability of data on social phenomena and collective behaviour. This has induced a deep change in social dynamics field, that moved from an essentially theoretical approach to a strongly data driven one. In such framework, the present work aims at exploring the collaboration dynamics and the organisational structures within the GitHub platform. Moreover, the purpose is using success and popularity as feedbacks to check whether some particular structures exist that are associated with more efficiency, better results and subsequently more innovative features in the development of the code. GitHub is based on the Git revision control system and is currently the most important platform for open source coding, counting millions of repositories and active users. Moreover, the complete timeline of GitHub activity is publicly accessible on the GitHub Archive website. GitHub is therefore a particularly suitable system to observe and analyse collective social behaviours and collaborative dynamics. The collaboration among users fosters an uninterrupted flow of new ideas which actualise in many different events such as the creation of new projects and updating of existing ones through code modifications. The analysis required a preliminary selection of the data downloaded from GitHub Archive in order to create a database containing all the necessary information about projects activity. The analysis carried out on this database was mostly inspired by previous research on innovation dynamics in the framework of complex systems. Every project was mapped in a network structure in order to
observe dynamically the development and the modifications of the code. Some metrics were defined that could estimate the collaboration degree among users and the organization of the workload within the developing branches. Other metrics were chosen in order to evaluate both the success and the popularity reached by a project and its potential innovation. Correlation analysis between the metrics and the indexes above mentioned allow for some evaluations about the interdependence between attention received and structural features of the projects. This thesis work follows up several quantitative analyses on GitHub presented in literature and proposes a new visualisation of internal structures and collaborative dynamics within GitHub projects. Moreover, identifying successful patterns could help in highlighting the most influential and pioneering projects and encouraging their development.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {mastersthesis}
}

The recent spread of social networks and ICT systems has allowed for a huge availability of data on social phenomena and collective behaviour. This has induced a deep change in social dynamics field, that moved from an essentially theoretical approach to a strongly data driven one. In such framework, the present work aims at exploring the collaboration dynamics and the organisational structures within the GitHub platform. Moreover, the purpose is using success and popularity as feedbacks to check whether some particular structures exist that are associated with more efficiency, better results and subsequently more innovative features in the development of the code. GitHub is based on the Git revision control system and is currently the most important platform for open source coding, counting millions of repositories and active users. Moreover, the complete timeline of GitHub activity is publicly accessible on the GitHub Archive website. GitHub is therefore a particularly suitable system to observe and analyse collective social behaviours and collaborative dynamics. The collaboration among users fosters an uninterrupted flow of new ideas which actualise in many different events such as the creation of new projects and updating of existing ones through code modifications. The analysis required a preliminary selection of the data downloaded from GitHub Archive in order to create a database containing all the necessary information about projects activity. The analysis carried out on this database was mostly inspired by previous research on innovation dynamics in the framework of complex systems. Every project was mapped in a network structure in order to
observe dynamically the development and the modifications of the code. Some metrics were defined that could estimate the collaboration degree among users and the organization of the workload within the developing branches. Other metrics were chosen in order to evaluate both the success and the popularity reached by a project and its potential innovation. Correlation analysis between the metrics and the indexes above mentioned allow for some evaluations about the interdependence between attention received and structural features of the projects. This thesis work follows up several quantitative analyses on GitHub presented in literature and proposes a new visualisation of internal structures and collaborative dynamics within GitHub projects. Moreover, identifying successful patterns could help in highlighting the most influential and pioneering projects and encouraging their development.

@article{pugliese2016exploring,
title = {Exploring the evolution of pathogens organised in discrete antigenic clusters},
author = { Martina Pugliese and Vittorio Loreto and Simone Pompei and Francesca Tria},
url = {http://iopscience.iop.org/article/10.1088/1742-5468/2016/09/093306/meta},
year = {2016},
date = {2016-01-01},
journal = {Journal of Statistical Mechanics: Theory and Experiment},
volume = {2016},
number = {9},
pages = {093306},
publisher = {IOP Publishing},
abstract = {We present a numerical model for the evolution of pathogens organised in discrete antigenic clusters, where individuals in the same clusters have the same fitness. The fitness of each cluster is a decreasing function of the total number of cluster members appeared in the population. Cluster transition is modelled with inclusion and exclusion of dynamical epistatic
effects. In both cases we observe a continuous transition, driven by the mutation rate, from a dynamics with single clusters alternating in time to the coexistence of many clusters in the population. The transition between the two regimes is investigated in terms of the key parameters of the model. We find that the location and the scaling of this transition can be explained in terms of the time of first appearance of a new cluster in the population. The presence of dynamical epistatic effects results in a shift of the value of the mutation rate where the transition occurs.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {article}
}

We present a numerical model for the evolution of pathogens organised in discrete antigenic clusters, where individuals in the same clusters have the same fitness. The fitness of each cluster is a decreasing function of the total number of cluster members appeared in the population. Cluster transition is modelled with inclusion and exclusion of dynamical epistatic
effects. In both cases we observe a continuous transition, driven by the mutation rate, from a dynamics with single clusters alternating in time to the coexistence of many clusters in the population. The transition between the two regimes is investigated in terms of the key parameters of the model. We find that the location and the scaling of this transition can be explained in terms of the time of first appearance of a new cluster in the population. The presence of dynamical epistatic effects results in a shift of the value of the mutation rate where the transition occurs.

@article{loreto2016emergence,
title = {On the Emergence of Syntactic Structures: Quantifying and Modeling Duality of Patterning},
author = { Vittorio Loreto and Pietro Gravino and Vito DP Servedio and Francesca Tria},
url = {http://onlinelibrary.wiley.com/doi/10.1111/tops.12193/full},
year = {2016},
date = {2016-01-01},
journal = {Topics in cognitive science},
volume = {8},
number = {2},
pages = {469--480},
publisher = {Wiley Online Library},
abstract = {The complex organization of syntax in hierarchical structures is one of the core design features of human language. Duality of patterning refers for instance to the organization of the meaningful elements in a language at two distinct levels: a combinatorial level where meaningless forms are combined into meaningful forms and a compositional level where meaningful forms are composed into larger lexical units. The question remains wide open regarding how such a structure could have emerged. Furthermore a clear mathematical framework to quantify this phenomenon is still lacking. The aim of this paper is that of addressing these two aspects in a self-consistent way. First, we introduce suitable measures to quantify the level of combinatoriality and compositionality in a language, and present a framework to estimate these observables in human natural languages. Second, we show that the theoretical predictions of a multi-agents modeling scheme, namely the Blending Game, are in surprisingly good agreement with empirical data. In the Blending Game a population of individuals plays language games aiming at success in communication. It is remarkable that the two sides of duality of patterning emerge simultaneously as a consequence of a pure cultural dynamics in a simulated environment that contains meaningful relations, provided a simple constraint on message transmission fidelity is also considered.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {article}
}

The complex organization of syntax in hierarchical structures is one of the core design features of human language. Duality of patterning refers for instance to the organization of the meaningful elements in a language at two distinct levels: a combinatorial level where meaningless forms are combined into meaningful forms and a compositional level where meaningful forms are composed into larger lexical units. The question remains wide open regarding how such a structure could have emerged. Furthermore a clear mathematical framework to quantify this phenomenon is still lacking. The aim of this paper is that of addressing these two aspects in a self-consistent way. First, we introduce suitable measures to quantify the level of combinatoriality and compositionality in a language, and present a framework to estimate these observables in human natural languages. Second, we show that the theoretical predictions of a multi-agents modeling scheme, namely the Blending Game, are in surprisingly good agreement with empirical data. In the Blending Game a population of individuals plays language games aiming at success in communication. It is remarkable that the two sides of duality of patterning emerge simultaneously as a consequence of a pure cultural dynamics in a simulated environment that contains meaningful relations, provided a simple constraint on message transmission fidelity is also considered.

@article{Mastroianni2015,
title = {Local Optimization Strategies in Urban Vehicular Mobility},
author = {Pierpaolo Mastroianni and Bernardo Monechi and Carlo Liberto and Gaetano Valenti and Vito DP Servedio and Vittorio Loreto},
url = {http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0143799},
doi = {doi:10.1371/journal.pone.0143799},
year = {2015},
date = {2015-12-15},
journal = {PloS one},
volume = {10},
number = {12},
pages = {e0143799},
publisher = {Public Library of Science},
abstract = {The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints—physical, environmental, social, economic—that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy) district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.},
keywords = {GPS data, human mobility, kreyon, local optimization, loreto, monechi, servedio, urban network},
pubstate = {published},
tppubtype = {article}
}

The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints—physical, environmental, social, economic—that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy) district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.

@article{Rodi2015,
title = {Optimal Learning Paths in Information Networks},
author = {Giovanna Chiara Rodi and Vittorio Loreto and Vito DP Servedio and Francesca Tria},
url = {http://www.nature.com/srep/2015/150601/srep10286/full/srep10286.html},
doi = {10.1038/srep10286},
year = {2015},
date = {2015-06-01},
journal = {Scientific Reports},
volume = {5},
number = {10286},
abstract = {Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.},
keywords = {innovation_dynamics, kreyon, learning_dynamics, loreto, rodi, servedio, tria},
pubstate = {published},
tppubtype = {article}
}

Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.

@article{Monechi2015,
title = {Congestion Transition in Air Traffic Networks},
author = {Bernardo Monechi, Vito DP Servedio and Vittorio Loreto },
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0125546},
doi = {10.1371/journal.pone.0125546},
year = {2015},
date = {2015-05-20},
journal = {PLoS ONE},
volume = {10},
number = {5},
pages = {e0125546},
abstract = {Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.},
keywords = {air traffic, complex_systems, loreto, monechi, servedio, transportation networks},
pubstate = {published},
tppubtype = {article}
}

Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

Contact languages are born out of the non-trivial interaction of two (or more) parent languages. Nowadays, the enhanced possibility of mobility and communication allows for a strong mixing of languages and cultures, thus raising the issue of whether there are any pure languages or cultures that are unaffected by contact with others. As with bacteria or viruses in biological evolution, the evolution of languages is marked by horizontal transmission; but to date no reliable quantitative tools to investigate these phenomena have been available. An interesting and well documented example of contact language is the emergence of creole languages, which originated in the contacts of European colonists and slaves during the 17th and 18th centuries in exogenous plantation colonies of especially the Atlantic and Indian Ocean. Here, we focus on the emergence of creole languages to demonstrate a dynamical process that mimics the process of creole formation in American and Caribbean plantation ecologies. Inspired by the Naming Game (NG), our modeling scheme incorporates demographic information about the colonial population in the framework of a non-trivial interaction network including three populations: Europeans, Mulattos/Creoles, and Bozal slaves. We show how this sole information makes it possible to discriminate territories that produced modern creoles from those that did not, with a surprising accuracy. The generality of our approach provides valuable insights for further studies on the emergence of languages in contact ecologies as well as to test specific hypotheses about the peopling and the population structures of the relevant territories. We submit that these tools could be relevant to addressing problems related to contact phenomena in many cultural domains: e.g., emergence of dialects, language competition and hybridization, globalization phenomena.

<p>Contact languages are born out of the non-trivial interaction of two (or more) parent languages. Nowadays, the enhanced possibility of mobility and communication allows for a strong mixing of languages and cultures, thus raising the issue of whether there are any pure languages or cultures that are unaffected by contact with others. As with bacteria or viruses in biological evolution, the evolution of languages is marked by horizontal transmission; but to date no reliable quantitative tools to investigate these phenomena have been available. An interesting and well documented example of contact language is the emergence of creole languages, which originated in the contacts of European colonists and slaves during the 17th and 18th centuries in exogenous plantation colonies of especially the Atlantic and Indian Ocean. Here, we focus on the emergence of creole languages to demonstrate a dynamical process that mimics the process of creole formation in American and Caribbean plantation ecologies. Inspired by the Naming Game (NG), our modeling scheme incorporates demographic information about the colonial population in the framework of a non-trivial interaction network including three populations: Europeans, Mulattos/Creoles, and Bozal slaves. We show how this sole information makes it possible to discriminate territories that produced modern creoles from those that did not, with a surprising accuracy. The generality of our approach provides valuable insights for further studies on the emergence of languages in contact ecologies as well as to test specific hypotheses about the peopling and the population structures of the relevant territories. We submit that these tools could be relevant to addressing problems related to contact phenomena in many cultural domains: e.g., emergence of dialects, language competition and hybridization, globalization phenomena.</p>

@article{Cuskley2015205,
title = {The adoption of linguistic rules in native and non-native speakers: Evidence from a Wug task},
author = {Christine Cuskley and Francesca Colaiori and Claudio Castellano and Vittorio Loreto and Martina Pugliese and Francesca Tria},
url = {http://www.sciencedirect.com/science/article/pii/S0749596X15000790},
doi = {http://dx.doi.org/10.1016/j.jml.2015.06.005},
issn = {0749-596X},
year = {2015},
date = {2015-01-01},
journal = {Journal of Memory and Language},
volume = {84},
pages = {205 - 223},
abstract = {Several recent theories have suggested that an increase in the number of non-native speakers in a language can lead to changes in morphological rules. We examine this experimentally by contrasting the performance of native and non-native English speakers in a simple Wug-task, showing that non-native speakers are significantly more likely to provide non -ed (i.e., irregular) past-tense forms for novel verbs than native speakers. Both groups are sensitive to sound similarities between new words and existing words (i.e., are more likely to provide irregular forms for novel words which sound similar to existing irregulars). Among both natives and non-natives, irregularizations are non-random; that is, rather than presenting as truly irregular inflectional strategies, they follow identifiable sub-rules present in the highly frequent set of irregular English verbs. Our results shed new light on how native and non-native learners can affect language structure.},
keywords = {castellano, colaiori, cuskley, experiment, kreyon, language_dynamics, loreto, pugliese, rules, Sociolinguistics, tria},
pubstate = {published},
tppubtype = {article}
}

Several recent theories have suggested that an increase in the number of non-native speakers in a language can lead to changes in morphological rules. We examine this experimentally by contrasting the performance of native and non-native English speakers in a simple Wug-task, showing that non-native speakers are significantly more likely to provide non -ed (i.e., irregular) past-tense forms for novel verbs than native speakers. Both groups are sensitive to sound similarities between new words and existing words (i.e., are more likely to provide irregular forms for novel words which sound similar to existing irregulars). Among both natives and non-natives, irregularizations are non-random; that is, rather than presenting as truly irregular inflectional strategies, they follow identifiable sub-rules present in the highly frequent set of irregular English verbs. Our results shed new light on how native and non-native learners can affect language structure.

@article{PhysRevE.91.012808b,
title = {General three-state model with biased population replacement: Analytical solution and application to language dynamics},
author = { Francesca Colaiori and Claudio Castellano and Christine F. Cuskley and Vittorio Loreto and Martina Pugliese and Francesca Tria},
url = {http://link.aps.org/doi/10.1103/PhysRevE.91.012808},
doi = {10.1103/PhysRevE.91.012808},
year = {2015},
date = {2015-01-01},
journal = {Phys. Rev. E},
volume = {91},
pages = {012808},
publisher = {American Physical Society},
abstract = {Empirical evidence shows that the rate of irregular usage of English verbs exhibits discontinuity as a function of their frequency: the most frequent verbs tend to be totally irregular. We aim to qualitatively understand the origin of this feature by studying simple agent-based models of language dynamics, where each agent adopts an inflectional state for a verb and may change it upon interaction with other agents. At the same time, agents are replaced at some rate by new agents adopting the regular form. In models with only two inflectional states (regular and irregular), we observe that either all verbs regularise irrespective of their frequency, or a continuous transition occurs between a low-frequency state, where the lemma becomes fully regular, and a high-frequency one, where both forms coexist. Introducing a third (mixed) state, wherein agents may use either form, we find that a third, qualitatively different behaviour may emerge, namely, a discontinuous transition in frequency. We introduce and solve analytically a very general class of three-state models that allows us to fully understand these behaviours in a unified framework. Realistic sets of interaction rules, including the well-known naming game (NG) model, result in a discontinuous transition, in agreement with recent empirical findings. We also point out that the distinction between speaker and hearer in the interaction has no effect on the collective behaviour. The results for the general three-state model, although discussed in terms of
language dynamics, are widely applicable.},
keywords = {castellano, colaiori, cuskley, kreyon, language_dynamics, language_games, loreto, modeling, naming_game, pugliese, tria},
pubstate = {published},
tppubtype = {article}
}

Empirical evidence shows that the rate of irregular usage of English verbs exhibits discontinuity as a function of their frequency: the most frequent verbs tend to be totally irregular. We aim to qualitatively understand the origin of this feature by studying simple agent-based models of language dynamics, where each agent adopts an inflectional state for a verb and may change it upon interaction with other agents. At the same time, agents are replaced at some rate by new agents adopting the regular form. In models with only two inflectional states (regular and irregular), we observe that either all verbs regularise irrespective of their frequency, or a continuous transition occurs between a low-frequency state, where the lemma becomes fully regular, and a high-frequency one, where both forms coexist. Introducing a third (mixed) state, wherein agents may use either form, we find that a third, qualitatively different behaviour may emerge, namely, a discontinuous transition in frequency. We introduce and solve analytically a very general class of three-state models that allows us to fully understand these behaviours in a unified framework. Realistic sets of interaction rules, including the well-known naming game (NG) model, result in a discontinuous transition, in agreement with recent empirical findings. We also point out that the distinction between speaker and hearer in the interaction has no effect on the collective behaviour. The results for the general three-state model, although discussed in terms of
language dynamics, are widely applicable.

@inproceedings{sakellariou2015maximum,
title = {Maximum entropy model for melodic patterns},
author = {Jason Sakellariou and and Francesca Tria and Loreto Vittorio and François Pachet},
url = {https://arxiv.org/abs/1610.03414},
year = {2015},
date = {2015-01-01},
booktitle = {ICML Workshop on Constructive Machine Learning},
abstract = {We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity.},
keywords = {kreyon},
pubstate = {published},
tppubtype = {inproceedings}
}

We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity.

@article{baronchelli2015individual,
title = {Individual biases, cultural evolution, and the statistical nature of language universals: The case of colour naming systems},
author = {Andrea Baronchelli and Vittorio Loreto and Andrea Puglisi},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125019},
year = {2015},
date = {2015-01-01},
journal = {PloS one},
volume = {10},
number = {5},
pages = {e0125019},
publisher = {Public Library of Science},
abstract = {Language universals have long been attributed to an innate Universal Grammar. An alternative explanation states that linguistic universals emerged independently in every language in response to shared cognitive or perceptual biases. A computational model has recently shown how this could be the case, focusing on the paradigmatic example of the universal properties of colour naming patterns, and producing results in quantitative agreement with the experimental data. Here we investigate the role of an individual perceptual bias in the framework of the model. We study how, and to what extent, the structure of the bias influences the corresponding linguistic universal patterns. We show that the cultural history of a group of speakers introduces population-specific constraints that act against the pressure for uniformity arising from the individual bias, and we clarify the interplay between these two forces.},
keywords = {kreyon, loreto},
pubstate = {published},
tppubtype = {article}
}

Language universals have long been attributed to an innate Universal Grammar. An alternative explanation states that linguistic universals emerged independently in every language in response to shared cognitive or perceptual biases. A computational model has recently shown how this could be the case, focusing on the paradigmatic example of the universal properties of colour naming patterns, and producing results in quantitative agreement with the experimental data. Here we investigate the role of an individual perceptual bias in the framework of the model. We study how, and to what extent, the structure of the bias influences the corresponding linguistic universal patterns. We show that the cultural history of a group of speakers introduces population-specific constraints that act against the pressure for uniformity arising from the individual bias, and we clarify the interplay between these two forces.

@phdthesis{MonechiPhDThesis2014,
title = {Complex Networks and Transport Systems: Application to Air Transport and Urban Mobility},
author = {Bernardo Monechi, Vito DP Servedio, Vittorio Loreto},
url = {http://www.phys.uniroma1.it/fisica/sites/default/files/DOTT_FISICA/MENU/03DOTTORANDI/TesiFin27/Monechi.pdf},
year = {2014},
date = {2014-12-20},
address = {Piazzale Aldo Moro 5, 00185, Roma (RO), Italy},
school = {"Sapienza" University of Rome},
abstract = {This thesis is devoted to the study of transportation systems by means of Complex Systems
and Complex Network Theories. Complex Networks are a tools of inestimable value
in human transportation studies since in most of the cases the means of transportation
used by individuals to move in space are bounded to move on a complex network. The
topological properties of transportation networks can influence both the ability of individuals
to move as well as their behavior in the environment, thus a characterization of
the network is mandatory in order to understand the properties of the considered system.
The two transportation systems that have been studied in this work are the Air Transport
System and the mobility of cars in a urban environment.
The analysis and modeling of the Air Transport System is the first and most extensive
part of this thesis. In particular we will try to characterize and study the networks in
which aircraft fly, exploiting these results to build a data-driven model of Air Traffic Control.
The second part of the thesis is a continuation of the studies performed during by Pierpaolo
Mastroianni during his Master Thesis. His work concerned the analysis of GPS
tracks data in the City of Rome and the inference of statistical laws characterizing the
behavior of car drivers. My contribution to his work is the development of a model capable
of explaining some of the results presented in the Master Thesis.},
keywords = {air traffic, big data, complex network, GPS data, human mobility, local optimization, loreto, monechi, servedio, transportation network},
pubstate = {published},
tppubtype = {phdthesis}
}

This thesis is devoted to the study of transportation systems by means of Complex Systems
and Complex Network Theories. Complex Networks are a tools of inestimable value
in human transportation studies since in most of the cases the means of transportation
used by individuals to move in space are bounded to move on a complex network. The
topological properties of transportation networks can influence both the ability of individuals
to move as well as their behavior in the environment, thus a characterization of
the network is mandatory in order to understand the properties of the considered system.
The two transportation systems that have been studied in this work are the Air Transport
System and the mobility of cars in a urban environment.
The analysis and modeling of the Air Transport System is the first and most extensive
part of this thesis. In particular we will try to characterize and study the networks in
which aircraft fly, exploiting these results to build a data-driven model of Air Traffic Control.
The second part of the thesis is a continuation of the studies performed during by Pierpaolo
Mastroianni during his Master Thesis. His work concerned the analysis of GPS
tracks data in the City of Rome and the inference of statistical laws characterizing the
behavior of car drivers. My contribution to his work is the development of a model capable
of explaining some of the results presented in the Master Thesis.

@inproceedings{Monechi2014,
title = {An Air Traffic Control Model Based Local Optimization over the Airways Network},
author = {Bernardo Monechi, Vito DP Servedio, Vittorio Loreto },
editor = {Dirk Schaefer},
url = {http://www.sesarinnovationdays.eu/sites/default/files/media/SIDs/SID%202014-04.pdf},
isbn = {978-2-87497-077-1},
year = {2014},
date = {2014-11-25},
booktitle = {Proceedings of the SESAR Innovation Days (2014)},
organization = {EUROCONTROL},
abstract = {The introduction of a new SESAR scenario in the European Airspace will impact the functioning and the performances of the current Air Traffic Management (ATM) System. The understanding of the features and the limits of the current system could be crucial in order to improve and
design the structure of the future ATM. In this paper we present some results of the \"Assessment of Critical Delay Patterns and Avalanche Dynamics” PhD project from the ComplexWorld Network. During this project we developed a model of Air Traffic
Control (ATC) based on Complex Network theory capable of reproducing the features of the real ATC in three European National Airspaces. We then developed an optimization algorithm
based on “Extremal Optimization” in order to build efficient and globally optimized planned trajectories. The ATC model is applied in order to study the efficiency of this new planned trajectories when subject to external perturbations and to compare them to the current situation.},
keywords = {air traffic, extremal optimization, local optimization, loreto, monechi, servedio, transportation networks},
pubstate = {published},
tppubtype = {inproceedings}
}

The introduction of a new SESAR scenario in the European Airspace will impact the functioning and the performances of the current Air Traffic Management (ATM) System. The understanding of the features and the limits of the current system could be crucial in order to improve and
design the structure of the future ATM. In this paper we present some results of the "Assessment of Critical Delay Patterns and Avalanche Dynamics” PhD project from the ComplexWorld Network. During this project we developed a model of Air Traffic
Control (ATC) based on Complex Network theory capable of reproducing the features of the real ATC in three European National Airspaces. We then developed an optimization algorithm
based on “Extremal Optimization” in order to build efficient and globally optimized planned trajectories. The ATC model is applied in order to study the efficiency of this new planned trajectories when subject to external perturbations and to compare them to the current situation.

@article{b,
title = {The dynamics of correlated novelties},
author = {Francesca Tria and Vittorio Loreto and Vito Domenico Pietro Servedio and Steven H. Strogatz},
url = {http://www.nature.com/srep/2014/140731/srep05890/full/srep05890.html},
year = {2014},
date = {2014-01-01},
journal = {Nature Scientific Reports},
volume = {4},
number = {5890},
publisher = {Nature Publishing Group},
abstract = {Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called expanding the adjacent possible . The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya\'s urn, predicts statistical laws for the rate at which novelties happen (Heaps\' law) and for the probability distribution on the space explored (Zipf\'s law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.},
keywords = {creativity, innovation, innovation_dynamics, kreyon, loreto, novelties, servedio, tria},
pubstate = {published},
tppubtype = {article}
}

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called expanding the adjacent possible . The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.

@article{b,
title = {Awareness and learning in participatory noise sensing},
author = {Martin Becker and Saverio Caminiti and Donato Fiorella and Louise Francis and Pietro Gravino and Mordechai Haklay and Andreas Hotho and Vittorio Loreto and Juergen Mueller and Ferdinando Ricchiuti and Vito D.P. Servedio and Alina Sirbu and Francesca Tria},
url = {http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0081638},
year = {2013},
date = {2013-01-01},
journal = {PLoS ONE},
volume = {8},
pages = {e81638-1--e81638-12},
abstract = {The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.},
keywords = {citizen_science, gravino, loreto, servedio, tria},
pubstate = {published},
tppubtype = {article}
}

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

@article{b,
title = {Cohesion, consensus and extreme information in opinion dynamics},
author = {Alina Sirbu and Vittorio Loreto and Vito Domenico Pietro Servedio and Francesca Tria},
url = {http://www.worldscientific.com/doi/abs/10.1142/S0219525913500355},
year = {2013},
date = {2013-01-01},
journal = {ADVANCES IN COMPLEX SYSTEM},
volume = {16},
publisher = {WORLD SCIENTIFIC PUBL CO PTE LTD},
abstract = {Opinion formation is an important element of social dynamics. It has been widely studied in the last years with tools from physics, mathematics and computer science. Here, a continuous model of opinion dynamics for multiple possible choices is analyzed. Its main features are the inclusion of disagreement and possibility of modulating external information/media effects, both from one and multiple sources. The interest is in identifying the effect of the initial cohesion of the population, the interplay between cohesion and media extremism, and the effect of using multiple external sources of information that can influence the system. Final consensus, especially with the external message, depends highly on these factors, as numerical simulations show. When no external input is present, consensus or segregation is determined by the initial cohesion of the population. Interestingly, when only one external source of information is present, consensus can be obtained, in general, only when this is extremely neutral, i.e., there is not a single opinion strongly promoted, or in the special case of a large initial cohesion and low exposure to the external message. On the contrary, when multiple external sources are allowed, consensus can emerge with one of them even when this is not extremely neutral, i.e., it carries a strong message, for a large range of initial conditions.},
keywords = {loreto, opinion_dynamics, servedio, tria},
pubstate = {published},
tppubtype = {article}
}

Opinion formation is an important element of social dynamics. It has been widely studied in the last years with tools from physics, mathematics and computer science. Here, a continuous model of opinion dynamics for multiple possible choices is analyzed. Its main features are the inclusion of disagreement and possibility of modulating external information/media effects, both from one and multiple sources. The interest is in identifying the effect of the initial cohesion of the population, the interplay between cohesion and media extremism, and the effect of using multiple external sources of information that can influence the system. Final consensus, especially with the external message, depends highly on these factors, as numerical simulations show. When no external input is present, consensus or segregation is determined by the initial cohesion of the population. Interestingly, when only one external source of information is present, consensus can be obtained, in general, only when this is extremely neutral, i.e., there is not a single opinion strongly promoted, or in the special case of a large initial cohesion and low exposure to the external message. On the contrary, when multiple external sources are allowed, consensus can emerge with one of them even when this is not extremely neutral, i.e., it carries a strong message, for a large range of initial conditions.

Human Influenza A virus undergoes recurrent changes in the hemagglutinin (HA) surface protein, primarily involved in the human antibody recognition. Relevant antigenic changes, enabling the virus to evade host immune response, have been recognized to occur in parallel to multiple mutations at antigenic sites in HA. Yet, the role of correlated mutations (epistasis) in driving the molecular evolution of the virus still represents a challenging puzzle. Further, though circulation at a global geographic level is key for the survival of Influenza A, its role in shaping the viral phylodynamics remains largely unexplored. Here we show, through a sequence based epidemiological model, that epistatic effects between amino acids substitutions, coupled with a reservoir that mimics worldwide circulating viruses, are key determinants that drive human Influenza A evolution. Our approach explains all the up-to-date observations characterizing the evolution of H3N2 subtype, including phylogenetic properties, nucleotide fixation patterns, and composition of antigenic clusters.

@article{b,
title = {Emergence of fast agreement in an overhearing population: The case of the naming game},
author = {Suman Kalyan Maity and Animesh Mukherjee and Francesca Tria and Vittorio Loreto},
year = {2013},
date = {2013-01-01},
journal = {EUROPHYSICS LETTERS},
volume = {101},
abstract = {The naming game (NG) describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary. This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing (i.e., at every time step of the game, a random set of N-delta individuals are chosen from the population who overhear the transmitted word from the speaker and accordingly reshape their inventories). When delta = 0 one recovers the behavior of the original NG. As one increases delta, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as log N as delta approaches 1. Copyright (C) EPLA, 2013},
keywords = {language_dynamics, loreto, tria},
pubstate = {published},
tppubtype = {article}
}

The naming game (NG) describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary. This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing (i.e., at every time step of the game, a random set of N-delta individuals are chosen from the population who overhear the transmitted word from the speaker and accordingly reshape their inventories). When delta = 0 one recovers the behavior of the original NG. As one increases delta, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as log N as delta approaches 1. Copyright (C) EPLA, 2013

@article{b,
title = {Opinion Dynamics with Disagreement and Modulated Information},
author = {Alina Sirbu and Vittorio Loreto and Vito Domenico Pietro Servedio and Francesca Tria},
year = {2013},
date = {2013-01-01},
journal = {JOURNAL OF STATISTICAL PHYSICS},
volume = {151},
pages = {218--237},
abstract = {Opinion dynamics concerns social processes through which populations or groups of individuals agree or disagree on specific issues. As such, modelling opinion dynamics represents an important research area that has been progressively acquiring relevance in many different domains. Existing approaches have mostly represented opinions through discrete binary or continuous variables by exploring a whole panoply of cases: e.g. independence, noise, external effects, multiple issues. In most of these cases the crucial ingredient is an attractive dynamics through which similar or similar enough agents get closer. Only rarely the possibility of explicit disagreement has been taken into account (i.e., the possibility for a repulsive interaction among individuals' opinions), and mostly for discrete or 1-dimensional opinions, through the introduction of additional model parameters. Here we introduce a new model of opinion formation, which focuses on the interplay between the possibility of explicit disagreement, modulated in a self-consistent way by the existing opinions' overlaps between the interacting individuals, and the effect of external information on the system. Opinions are modelled as a vector of continuous variables related to multiple possible choices for an issue. Information can be modulated to account for promoting multiple possible choices. Numerical results show that extreme information results in segregation and has a limited effect on the population, while milder messages have better success and a cohesion effect. Additionally, the initial condition plays an important role, with the population forming one or multiple clusters based on the initial average similarity between individuals, with a transition point depending on the number of opinion choices.},
keywords = {loreto, opinion_dynamics, servedio, tria},
pubstate = {published},
tppubtype = {article}
}

Opinion dynamics concerns social processes through which populations or groups of individuals agree or disagree on specific issues. As such, modelling opinion dynamics represents an important research area that has been progressively acquiring relevance in many different domains. Existing approaches have mostly represented opinions through discrete binary or continuous variables by exploring a whole panoply of cases: e.g. independence, noise, external effects, multiple issues. In most of these cases the crucial ingredient is an attractive dynamics through which similar or similar enough agents get closer. Only rarely the possibility of explicit disagreement has been taken into account (i.e., the possibility for a repulsive interaction among individuals' opinions), and mostly for discrete or 1-dimensional opinions, through the introduction of additional model parameters. Here we introduce a new model of opinion formation, which focuses on the interplay between the possibility of explicit disagreement, modulated in a self-consistent way by the existing opinions' overlaps between the interacting individuals, and the effect of external information on the system. Opinions are modelled as a vector of continuous variables related to multiple possible choices for an issue. Information can be modulated to account for promoting multiple possible choices. Numerical results show that extreme information results in segregation and has a limited effect on the population, while milder messages have better success and a cohesion effect. Additionally, the initial condition plays an important role, with the population forming one or multiple clusters based on the initial average similarity between individuals, with a transition point depending on the number of opinion choices.

@article{b,
title = {Challenges in complex systems science},
author = {Maxi San Miguel and Jeffrey H. Johnson and Janosz Kertesz and Kimmo Kaski and Albert Diaz-Guilera and Robert S. Mackay and Vittorio Loreto and Peter Erdi and Dirk Helbing},
year = {2012},
date = {2012-01-01},
journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS},
volume = {214},
pages = {245--271},
publisher = {SPRINGER HEIDELBERG},
abstract = {FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.},
keywords = {complex systems, loreto, social_dynamics},
pubstate = {published},
tppubtype = {article}
}

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.

@article{b,
title = {Complex structures and semantics in free word association},
author = {Pietro Gravino and Vito D.P. Servedio and Alain Barrat and Vittorio Loreto},
url = {http://www.worldscinet.com/acs/15/1503n04/S0219525912500543.html, http://www.scopus.com/inward/record.url?eid=2-s2.0-84861899272&partnerID=65&md5=4d6ecfe66508c0a3cf8bba8dae67c997
http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/2012/S0219525912500543.pdf},
year = {2012},
date = {2012-01-01},
journal = {ADVANCES IN COMPLEX SYSTEM},
volume = {15},
pages = {1250054--1250075},
publisher = {WORLD SCIENTIFIC PUBL CO PTE LTD},
abstract = {We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.},
keywords = {complex_networks, gravino, language_dynamics, loreto, servedio},
pubstate = {published},
tppubtype = {article}
}

We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.

This article adopts the category game model, which simulates the origins and evolution of linguistic categories in a group of artificial agents, to evaluate the effect of social structure on linguistic categorization. Based on the simulation results in a number of typical networks, we examine the isolating and collective effects of some structural features, including average degree, shortcuts, and level of centrality, on the categorization process. This study extends the previous simulations mainly on lexical evolution, and illustrates a general framework to systematically explore the effect of social structure on language evolution.

@article{b,
title = {Language Dynamics},
author = {Andrea Baronchelli and Vittorio Loreto and Francesca Tria},
url = {http://www.worldscinet.com/acs/15/1503n04/S0219525912030026.html
http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/2005/Baronchelli_EurJourPhys_2005.pdf},
year = {2012},
date = {2012-01-01},
journal = {ADVANCES IN COMPLEX SYSTEM},
volume = {15},
pages = {1203002--12030011},
publisher = {WORLD SCIENTIFIC PUBL CO PTE LTD},
abstract = {Thirty authors of different disciplines, ranging from cognitive science and linguistics to mathematics and physics, address the topic of language origin and evolution. Language dynamics is investigated through an interdisciplinary effort, involving field and synthetic experiments, modelling and comparison of the theoretical predictions with empirical data. The result consists in new insights that significantly contribute to the ongoing debate on the origin and the evolution of language. In this Topical Issue the state of the art of this novel and fertile approach is reported by major experts of the field.},
keywords = {language_dynamics, loreto, tria},
pubstate = {published},
tppubtype = {article}
}

Thirty authors of different disciplines, ranging from cognitive science and linguistics to mathematics and physics, address the topic of language origin and evolution. Language dynamics is investigated through an interdisciplinary effort, involving field and synthetic experiments, modelling and comparison of the theoretical predictions with empirical data. The result consists in new insights that significantly contribute to the ongoing debate on the origin and the evolution of language. In this Topical Issue the state of the art of this novel and fertile approach is reported by major experts of the field.

@article{b,
title = {Manifesto of computational social science},
author = {Rosaria Conte and Nigel Gilbert and Giulia Bonelli and Claudio Cioffi-Revilla and Guillaume Deffuant and Janosz Kertesz and Vittorio Loreto and Susy Moat and Jean-Pierre Nadal and Ancho Sanchez and Andrzej Nowak and Andreas Flache and Maxi San Miguel and Dirk Helbing},
year = {2012},
date = {2012-01-01},
journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS},
volume = {214},
pages = {325--346},
abstract = {The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.},
keywords = {loreto, social_dynamics},
pubstate = {published},
tppubtype = {article}
}

The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.

@article{b,
title = {Naming a structured world: a cultural route to duality of patterning},
author = {Francesca Tria and Bruno Galantucci and Vittorio Loreto},
url = {http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0037744, http://www.scopus.com/inward/record.url?eid=2-s2.0-84862532680&partnerID=65&md5=596aaa8cb591f6d2d7e2a31bcdf3213e, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000305652700006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=0c7ff228ccbaaa74236f48834a34396a},
year = {2012},
date = {2012-01-01},
journal = {PLOS ONE},
pages = {e37744-1--e37744-8},
abstract = {The lexicons of human languages organize their units at two distinct levels. At a first combinatorial level, meaningless forms (typically referred to as phonemes) are combined into meaningful units (typically referred to as morphemes). Thanks to this, many morphemes can be obtained by relatively simple combinations of a small number of phonemes. At a second compositional level of the lexicon, morphemes are composed into larger lexical units, the meaning of which is related to the individual meanings of the composing morphemes. This duality of patterning is not a necessity for lexicons and the question remains wide open regarding how a population of individuals is able to bootstrap such a structure and the evolutionary advantages of its emergence. Here we address this question in the framework of a multi-agents model, where a population of individuals plays simple naming games in a conceptual environment modeled as a graph. We demonstrate that errors in communication as well as a blending repair strategy, which crucially exploits a shared conceptual representation of the environment, are sufficient conditions for the emergence of duality of patterning, that can thus be explained in a pure cultural way. Compositional lexicons turn out to be faster to lead to successful communication than purely combinatorial lexicons, suggesting that meaning played a crucial role in the evolution of language.},
keywords = {language_dynamics, loreto, tria},
pubstate = {published},
tppubtype = {article}
}

The lexicons of human languages organize their units at two distinct levels. At a first combinatorial level, meaningless forms (typically referred to as phonemes) are combined into meaningful units (typically referred to as morphemes). Thanks to this, many morphemes can be obtained by relatively simple combinations of a small number of phonemes. At a second compositional level of the lexicon, morphemes are composed into larger lexical units, the meaning of which is related to the individual meanings of the composing morphemes. This duality of patterning is not a necessity for lexicons and the question remains wide open regarding how a population of individuals is able to bootstrap such a structure and the evolutionary advantages of its emergence. Here we address this question in the framework of a multi-agents model, where a population of individuals plays simple naming games in a conceptual environment modeled as a graph. We demonstrate that errors in communication as well as a blending repair strategy, which crucially exploits a shared conceptual representation of the environment, are sufficient conditions for the emergence of duality of patterning, that can thus be explained in a pure cultural way. Compositional lexicons turn out to be faster to lead to successful communication than purely combinatorial lexicons, suggesting that meaning played a crucial role in the evolution of language.

@article{b,
title = {On the origin of the hierarchy of color names},
author = {Vittorio Loreto and Animesh Mukherjee and Francesca Tria},
url = {http://www.pnas.org/content/early/2012/04/09/1113347109.abstract, http://www.scopus.com/inward/record.url?eid=2-s2.0-84860819374&partnerID=65&md5=d7b06adcaee23e02cd4e3f3eeb6be15c, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000303602100019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=0c7ff228ccbaaa74236f48834a34396a
http://samarcanda.phys.uniroma1.it/vittorioloreto/PAPERS/2012/PNAS-2012-Loreto-1113347109.pdf},
year = {2012},
date = {2012-01-01},
journal = {PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (PNAS)},
abstract = {One of the fundamental problems in cognitive science is how humans categorize the visible color spectrum. The empirical evidence of the existence of universal or recurrent patterns in color naming across cultures is paralleled by the observation that color names begin to be used by individual cultures in a relatively fixed order. The origin of this hierarchy is largely unexplained. Here we resort to multiagent simulations, where a population of individuals, subject to a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference, categorizes and names colors through a purely cultural negotiation in the form of language games. We found that the time needed for a population to reach consensus on a color name depends on the region of the visible color spectrum. If color spectrum regions are ranked according to this criterion, a hierarchy with [red, (magenta)-red], [violet], [green/yellow], [blue], [orange], and [cyan], appearing in this order, is recovered, featuring an excellent quantitative agreement with the empirical observations of the WCS. Our results demonstrate a clear possible route to the emergence of hierarchical color categories, confirming that the theoretical modeling in this area has now attained the required maturity to make significant contributions to the ongoing debates concerning language universals.},
keywords = {language_dynamics, loreto, tria},
pubstate = {published},
tppubtype = {article}
}

One of the fundamental problems in cognitive science is how humans categorize the visible color spectrum. The empirical evidence of the existence of universal or recurrent patterns in color naming across cultures is paralleled by the observation that color names begin to be used by individual cultures in a relatively fixed order. The origin of this hierarchy is largely unexplained. Here we resort to multiagent simulations, where a population of individuals, subject to a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference, categorizes and names colors through a purely cultural negotiation in the form of language games. We found that the time needed for a population to reach consensus on a color name depends on the region of the visible color spectrum. If color spectrum regions are ranked according to this criterion, a hierarchy with [red, (magenta)-red], [violet], [green/yellow], [blue], [orange], and [cyan], appearing in this order, is recovered, featuring an excellent quantitative agreement with the empirical observations of the WCS. Our results demonstrate a clear possible route to the emergence of hierarchical color categories, confirming that the theoretical modeling in this area has now attained the required maturity to make significant contributions to the ongoing debates concerning language universals.